Introductory mathematics
:This article is a continuation of Elementary mathematics Believe it or not the basis of all of mathematics is nothing more than the simple function. :Next(0)=1 :Next(1)=2 :Next(2)=3 :Next(3)=4 This defines the . Natural numbers are those used for counting. :These have the very convenient property of being . That means that if a1-3=x for which there is no answer among natural numbers. To provide an answer mathematicians generalize to the set of all which includes negative integers. :The absolute value of is defined as |x| = \left\{ \begin{array}{rl} x, & \text{if } x \geq 0 \\ -x, & \text{if } x < 0. \end{array}\right. :The study of integers is called . ::A prime number is a number that can only be divided by itself and one. If a, b, c, and d are primes then the of abc and c2d is abc2d. (See ) (See ) is defined as repeated addition, and its inverse is . But this leads to equations like 3/2=x for which there is no answer. The solution is to generalize to the set of which include fractions (See ). Any number which isnt rational is . : . :(Addition and multiplication are fast but division is slow .) (See ) is defined as repeated multiplication, and its inverses are and . :0^0 = 1. See . When a quantity, like the charge of a single electron, becomes so small that it is insignificant we, quite justifiably, treat it as though it were zero. A quantity that can be treated as though it were zero, even though it very definitely is not, is called infinitesimal. If q is a finite ( q \cdot 1 ) amount of charge then using dq would be an infinitesimal ( q \cdot 1/\infty ) amount of charge. See Likewise when a quantity becomes so large that a regular finite quantity becomes insignificant then we call it infinite. We would say that the mass of the ocean is infinite ( M \cdot \infty ) . But compared to the mass of the Milky Way galaxy our ocean is insignificant. So we would say the mass of the Galaxy is doubly infinite ( M \cdot \infty^2 ) . Infinity and the infinitesimal are called . Hyperreals behave, in every way, exactly like real numbers. For example, 2 \cdot \infty is exactly twice as big as \infty. In reality, the mass of the ocean is a real number so it is hardly surprising that it behaves like one. See and Back to top Vectors The one dimensional number line can be generalized to a multidimensional thereby creating multidimensional math (i.e. ). : \mathbb{R}^3 is the \mathbb{R} \times \mathbb{R} \times \mathbb{R}. A is a with and (multiplication of a vector and a belonging to a . :If {\mathbf e_1} , {\mathbf e_2} , {\mathbf e_3} are :and {\mathbf u} , {\mathbf v} , {\mathbf x} are arbitrary vectors then we can (and usually do) write: ::'' \mathbf{u} = u_1 \mathbf{e_1} + u_2 \mathbf{e_2} + u_3 \mathbf{e_3} = \begin{bmatrix} u_1 & u_2 & u_3 \end{bmatrix} '' ::'' \mathbf{v} = v_1 \mathbf{e_1} + v_2 \mathbf{e_2} + v_3 \mathbf{e_3} = \begin{bmatrix} v_1 & v_2 & v_3 \end{bmatrix} '' ::'' \mathbf{x} = x_1 \mathbf{e_1} + x_2 \mathbf{e_2} + x_3 \mathbf{e_3} = \begin{bmatrix} x_1 & x_2 & x_3 \end{bmatrix} '' The norm of vector \mathbf{v} is denoted \|\mathbf{v}\|. The double bars are used to avoid confusion with the absolute value of the function. :In the norm doesnt depend on the choice of coordinate system. As a result, rigid objects can rotate in Euclidean space. See proof of the to the right. :: \|\mathbf{v}\| = \sqrt{v_1^2 + v_2^2 + v_3^2} Back to top Dot product (a ): \mathbf{u} \cdot \mathbf{v} = \| \mathbf{u} \|\ \| \mathbf{v}\| \cos(\theta) = u_1 v_1 + u_2 v_2 + u_3 v_3 : \mathbf{u}\cdot\mathbf{v} = \begin{bmatrix}u_1 \mathbf{e_1} \\ u_2 \mathbf{e_2} \\ u_3 \mathbf{e_3} \end{bmatrix} \begin{bmatrix}v_1 \mathbf{e_1} & v_2 \mathbf{e_2} & v_3 \mathbf{e_3} \end{bmatrix} = \begin{bmatrix}u_1 v_1 + u_2 v_2 + u_3 v_3 \end{bmatrix} :Only parallel components multiply. :::Two vectors are orthogonal if \mathbf{u \cdot v} = 0. ::::In Euclidean space \|\mathbf{v}\|^2 = \mathbf{v}\cdot\mathbf{v}. Back to top Cross product : \mathbf a\times\mathbf b=\|\mathbf a\|\|\mathbf b\|\sin(\theta)\,\mathbf n Unlike the dot product, it is only defined in \R^3 (that is, three dimensions). It is commonly used in physics, engineering, vector calculus, and linear algebra. Back to top = Functions = " that takes an input x'', and returns a single corresponding output ''f(x'').]] ''f in the Cartesian plane, consisting of all points with coordinates of the form . The property of having one output for each input is represented geometrically by the fact that each vertical line (such as the yellow line through the origin) has exactly one crossing point with the curve.]] From Wikipedia:Function (mathematics) In mathematics, a '''function'The words map or mapping, transformation, correspondence, and operator are often used synonymously. . is a relation between a set of inputs and a set of permissible outputs with the property that each input is related to exactly one output. An example is the function f(x)=x^2 that relates each real number x'' to its square ''x''2. The output of a function ''f corresponding to an input x'' is denoted by ''f(x'') (read "''f of x''"). In this example, if the input is −3, then the output is 9, and we may write . See Tutorial:Evaluate by Substitution. Likewise, if the input is 3, then the output is also 9, and we may write . (The same output may be produced by more than one input, but each input gives only one output.) The input variable(s) are sometimes referred to as the argument(s) of the function. Back to top Euclids "common notions" : From Wikipedia:Euclidean geometry: Things that do not differ from one another are equal to one another Things that are equal to the same thing are also equal to one another | then a=c |} If equals are added to equals, then the wholes are equal | then a+c=b+d |} If equals are subtracted from equals, then the remainders are equal | then a-c=b-d |} The whole is greater than the part. Back to top Elementary algebra : From Wikipedia:Elementary algebra: Elementary algebra builds on and extends arithmetic by introducing letters called variables to represent general (non-specified) numbers. Algebraic expressions may be evaluated and simplified, based on the basic properties of arithmetic operations ( , , , and ). For example, *Added terms are simplified using coefficients. For example, x + x + x can be simplified as 3x (where 3 is a numerical coefficient). *Multiplied terms are simplified using exponents. For example, x \times x \times x is represented as x^3 *Like terms are added together,Andrew Marx, Shortcut Algebra I: A Quick and Easy Way to Increase Your Algebra I Knowledge and Test Scores, Publisher Kaplan Publishing, 2007, , 9781419552885, 288 pages, page 51 for example, 2x^2 + 3ab - x^2 + ab is written as x^2 + 4ab , because the terms containing x^2 are added together, and, the terms containing ab are added together. *Brackets can be "multiplied out", using . For example, x (2x + 3) can be written as (x \times 2x) + (x \times 3) which can be written as 2x^2 + 3x *Expressions can be factored. For example, 6x^5 + 3x^2 , by dividing both terms by 3x^2 can be written as 3x^2 (2x^3 + 1) For any function f , if a=b then: * f(a) = f(b) * a + c = b + c * ac = bc * a^c = b^c Back to top Trigonometry The reduces to the when gamma=90 degrees : c^2 = a^2 + b^2 - 2ab\cos\gamma, The (also known as the "sine rule") for an arbitrary triangle states: : \frac{a}{\sin A} = \frac{b}{\sin B} = \frac{c}{\sin C} = \frac{abc}{2\Delta}, where \Delta is the area of the triangle : \mbox{Area} = \Delta = \frac{1}{2}a b\sin C. The : : \frac{a-b}{a+b}=\frac{\tan\left\tfrac{1}{2}(A-B)\right}{\tan\left\tfrac{1}{2}(A+B)\right} Back to top Right triangles A right triangle is a triangle with gamma=90 degrees. For small values of x, sin x ≈ x. (If x is in radians). Back to top Polynomials :From Wikipedia:Polynomial: A can always be written in the form : polynomial = Z(x) = a_0 + a_1 x + a_2 x^2 + \dotsb + a_{n-1}x^{n-1} + a_n x^n where a_0, \ldots, a_n are constants called coefficients and n'' is the of the polynomial. :A is a polynomial of degree one. Each individual is the product of the and a variable raised to a nonnegative integer power. :A has only one term. :A has 2 terms. A root (or zero) of a function is a value of x for which Z(x)=0. :: Z(x) = a_n(x - z_1)(x - z_2)\dotsb(x - z_n) :: :The roots of the formula ax^2+bx+c=0 are given by the : :: (x+y)^n = {n \choose 0}x^n y^0 + {n \choose 1}x^{n-1}y^1 + {n \choose 2}x^{n-2}y^2 + \cdots + {n \choose n-1}x^1 y^{n-1} + {n \choose n}x^0 y^n, :Where \binom{n}{k} = \frac{n!}{k! (n-k)!}. See x^2 - y^2 = (x + y)(x - y) x^2 + y^2 = (x + yi)(x - yi) The states that the remainder of the division of a polynomial Z(x) by the linear polynomial x-a is equal to Z(a). See . Determining the value at Z(a) is sometimes easier if we use ( ) by writing the polynomial in the form : Z(x) = a_0 + x(a_1 + x(a_2 + \cdots + x(a_{n-1} + x(a_n)))). Back to top Calculus Integration :See also: and The ' ' is a generalization of multiplication. :For example: a unit mass dropped from point x2 to point x1 will release energy. :The usual equation is is a simple multiplication. We just multiply the strength of gravity times the distance that the object falls and the result is how much energy is released: :: gravity \cdot (x_2 - x_1) = energy :But that equation cant be used if the strength of gravity is itself a function of x. :The strength of gravity at x1 would be different than it is at x2. :And in reality gravity really does depend on x (x is the distance from the center of the earth): :: gravity(x) = 1/x^2 (See .) :However, the corresponding is easily solved: :: \int_{x_1}^{x_2} gravity(x) \cdot dx The fundamental theorem of Calculus is: : \int_{x_1}^{x_2} f(x) \cdot dx \quad = \quad F(x_2)-F(x_1) F(x) is called the . ( ) : \int f(x) \cdot dx = F(x) Solving the definite integral is easy: :: \int k \cdot x^y \cdot dx \quad = \quad k \cdot \int x^y \cdot dx \quad = \quad k \cdot \frac{x^{y+1}}{y+1} :where ''k and y'' are arbitrary constants. (Units (feet, mm...) behave exactly like constants.) :And most conveniently: : \int \bigg (f(x) + g(x) \bigg) \cdot dx = \int f(x) \cdot dx + \int g(x) \cdot dx The integral of a function is equal to the area under the curve. :When the "curve" is a constant (in other words, k•x0) then the integral reduces to ordinary multiplication. Back to top Differentiation Differentiation is the opposite of integration just as division is the opposite of multiplication. The of the integral of f(x) is just f(x). The derivative of a function at any point is equal to the slope of the function at that point. : f'(x)=\frac{f(x+dx)-f(x)}{dx}. The equation of the line tangent to a function at point a is : y(x) = f(a) + f'(a)(x-a) The derivative of f(x) where f(x) = k•xy is : f'(x) = {df \over dx} = {d(k \cdot x^y) \over dx} \quad = \quad k \cdot {d(x^y) \over dx} \quad = \quad k \cdot y \cdot x^{y-1} :The derivative of a k \cdot x^0 is k \cdot 0 \cdot x^{-1} :The integral of x^{-1} is ln(x) ex = y = dy/dx dx = dy/y = 1/y * dy ∫ (1/y)dy = ∫ dx = x = ln(y) . See for the derivative of a function of a function: : f(g(x))' = \frac{df}{dg} \cdot \frac{dg}{dx} : : (g \cdot h)' = \frac{(g+dg) \cdot (h+dh) - g \cdot h}{dx} = g' \cdot h + g \cdot h' (because dh \cdot dg is negligible) The : : \bigg( \frac{g(x)}{h(x)} \bigg)' = \bigg( g \cdot \frac{1}{h} \bigg)' = g' \cdot \frac{1}{h} + g \cdot \frac{-h'}{h^2} = \frac{g' \cdot h - g \cdot h'}{h^2} Back to top Taylor & Maclaurin series If we know the value of a at x=0 (smooth means all its derivatives are ) and we also know the value of all of its derivatives at x=0 then we can determine the value at any other point ''x by using the . ("!" means ) : a_0 x^0 + a_1 x^1 + a_2 x^2 + a_3 x^3 \cdots \quad \text{where} \quad a_n = {f^{(n)}(0) \over n!} The proof of this is actually quite simple. Plugging in a value of x=0 causes all terms but the first to become zero. So, assuming that such a function exists, a0 must be the value of the function at x=0. Simply differentiate both sides of the equation and repeat for the next term. And so on. We can easily determine the Maclaurin series expansion of the e^x (because it is equal to its own derivative). : e^x = \sum_{n = 0}^{\infty} {x^n \over n!} = {x^0 \over 0!} + {x^1 \over 1!} + {x^2 \over 2!} + {x^3 \over 3!} + {x^4 \over 4!} + \cdots ::The above holds true even if x is a matrix. See And and (because cosine is the derivative of sine which is the derivative of -cosine) : \cos x = \frac{x^0}{0!} - \frac{x^2}{2!} + \frac{x^4}{4!} - \frac{x^6}{6!} + \cdots : \sin x = \frac{x^1}{1!} - \frac{x^3}{3!} + \frac{x^5}{5!} - \frac{x^7}{7!} + \cdots Back to top Fourier Series The Maclaurin series cant be used for a discontinuous function like a square wave because it is not differentiable. ( make it possible to differentiate functions whose derivatives do not exist in the classical sense. See .) But remarkably we can use the to expand it or any other into an infinite sum of sine waves each of which is fully ! ::The reason this works is because sine and cosine are . Back to top Partial derivatives and generalize derivatives and integrals to multiple dimensions. The partial derivative with respect to one variable \frac{\part f(x,y)}{\part x} is found by simply treating all other variables as though they were constants. Multiple integrals are found the same way. Let f(x, y, z) be a (for example an elevation map). Gradient The of the elevation map is a vector that points "downhill" with a magnitude equal to the of the function at that point. The vectors are called flux lines. The gradient never goes in circles. : \operatorname{grad}(f) = \nabla f = \frac{\partial f}{\partial x} \mathbf{i} + \frac{\partial f}{\partial y} \mathbf{j} + \frac{\partial f}{\partial z} \mathbf{k} = \mathbf{F} Divergence The of the map of the is positive at places where flux lines begin and negative at places where flux lines end. : \operatorname{div}\,\mathbf{F} = {\color{red} \nabla\cdot\mathbf{F} } = \left( \frac{\partial}{\partial x}, \frac{\partial}{\partial y}, \frac{\partial}{\partial z} \right) \cdot (F_x,F_y,F_z) = \frac{\partial F_x}{\partial x} +\frac{\partial F_y}{\partial y} +\frac{\partial F_z}{\partial z}. Curl The of a vector field describes how much the flux lines are twisted. (The flux lines may even go in circles.) : \text{curl} (\mathbf{F}) = {\color{blue} \nabla \times \mathbf{F} } = \begin{vmatrix} \mathbf{i} & \mathbf{j} & \mathbf{k} \\ {\frac{\partial}{\partial x}} & {\frac{\partial}{\partial y}} & {\frac{\partial}{\partial z}} \\ F_x & F_y & F_z \end{vmatrix} : \text{curl}( \mathbf{F}) = \left(\frac{\partial F_z}{\partial y} - \frac{\partial F_y}{\partial z}\right) \mathbf{i} + \left(\frac{\partial F_x}{\partial z} - \frac{\partial F_z}{\partial x}\right) \mathbf{j} + \left(\frac{\partial F_y}{\partial x} - \frac{\partial F_x}{\partial y}\right) \mathbf{k} In 2 dimensions this reduces to a single scalar : \text{curl}( \mathbf{F}) = \left(\frac{\partial F_y}{\partial x} - \frac{\partial F_x}{\partial y}\right) The curl of the gradient is always zero. Green's theorem You can think of each field line as ending in a single unit of charge. states that if you want to know how many field lines exit a region then you can either count how many lines cross the boundary (perform a line integral) or you can simply count the number of charges within that region. See . A version of Green's theorem also works for curl. Green's theorem is an extremely important result that is used widely in more advanced mathematics. Green's theorem is perfectly obvious when dealing with vector fields but it is a great deal less obvious when applied to complex valued functions in the complex plane. Back to top = Intermediate mathematics = :See Intermediate mathematics = References =